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arXiv · 2407.03391

Soft Begging: Modular and Efficient Shielding of LLMs against Prompt Injection and Jailbreaking based on Prompt Tuning

Simon Ostermann·Kevin Baum·Christoph Endres·Julia Masloh·Patrick Schramowski
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Abstract

Prompt injection (both direct and indirect) and jailbreaking are now recognized as significant issues for large language models (LLMs), particularly due to their potential for harm in application-integrated contexts. This extended abstract explores a novel approach to protecting LLMs from such attacks, termed "soft begging." This method involves training soft prompts to counteract the effects of corrupted prompts on the LLM's output. We provide an overview of prompt injections and jailbreaking, introduce the theoretical basis of the "soft begging" technique, and discuss an evaluation of its effectiveness.

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Simon Ostermann, Kevin Baum, Christoph Endres, Julia Masloh, Patrick Schramowski. 2024-07-03. Soft Begging: Modular and Efficient Shielding of LLMs against Prompt Injection and Jailbreaking based on Prompt Tuning. https://arxiv.org/abs/2407.03391

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